Write a goal
Skill hamza-ali-shahjahan/hamzaish/factory/skills/write-a-goal
Turn a rough ambition into a measurable, REACHABLE goal — a capability statement, a precisely-named metric, ≥2 numeric evals, an acceptance rule, and non-goals — with a feasibility check that catches impossible or game-able targets before you chase them. Pair with /goal (which pursues a goal autonomously).From its SKILL.md
npx -y skills add hamza-ali-shahjahan/hamzaish --skill write-a-goalAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 5 stars5 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
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/write-a-goal
Usage: /write-a-goal <the ambition, or what "good" looks like>
A goal you can't measure is a wish. A goal you can't reach is a trap. A goal a
degenerate output can fake is a lie. This skill turns an ambition into a goal that
/goal can autonomously pursue and a fresh-eyes agent can actually certify —
and it refuses to hand you a target that's impossible or game-able.
The shape a good goal must clear
- Capability statement — one sentence: "Given X, the system does Y." Concrete, observable.
- The exact metric — the single thing measured, named precisely enough that two people would compute the same number (see Check A).
- ≥2 numeric evals — real test cases, each with a target number and how it's scored. One eval is an anecdote; two+ is a bar.
- Acceptance rule — when is it done? (e.g. "every eval ≥ target on a fresh run, confirmed by a fresh-eyes agent.")
- Non-goals — what this explicitly is not, so the loop doesn't gold-plate the wrong thing.
The two checks that make or break it (hard-won)
Check A — Pin the exact metric (kill vague percentages)
"Make it 99% similar" is meaningless until you answer similar HOW. The same artifact can score 20% or 95% depending on the measure. Force the choice:
- Name the measure (e.g. visual SSIM on edge maps, not "looks the same"; structural token overlap, not "matches the design"; p95 latency, not "fast").
- State how it's computed and what tool reports it.
- If one number hides several things, split it (e.g. "fidelity" → visual + structural + copy).
- Robustness clause: require the metric to resist gaming. Ask "what's the dumbest output that could score high?" — if a blank/empty/degenerate result can fake the target, the metric is broken; add a guard before you trust it.
Check B — Feasibility / ceiling check (don't set a trap)
Before committing the target, pressure-test it against what the architecture can actually reach:
- What's the theoretical ceiling for this approach? (e.g. a small local model reconstructing from a text description can't pixel-match a real site — the inputs it's missing cap it far below 100%.)
- Is the target inside that ceiling, with headroom? If not, say so plainly and propose a reachable target instead — never accept an impossible number silently.
- Name the lever that would raise the ceiling (a different input, a different model class) vs. the levers that just optimize within it — so effort goes where it pays.
What you do as the assistant when this is invoked
- Extract the ambition — what does the user actually want, and what would make them say "yes, that's it"? Ask at most one clarifying question if it's truly ambiguous.
- Draft the 5-part goal (capability · metric · ≥2 evals · acceptance · non-goals).
- Run Check A — pin the exact metric; split vague numbers; add the robustness guard.
- Run Check B — feasibility. If the target is unreachable or game-able, lead with that, and offer a reachable, robust alternative target (recommended-first).
- Output the goal spec as a small artifact (capability, metric+tool, evals with numbers, acceptance, non-goals, and the feasibility verdict).
- Offer to hand off to
/goalto pursue it autonomously, or/full-cycleif it's a build.
Anti-patterns — real cases this skill exists to prevent
- Vague + impossible: "Replicate this homepage to 99%." Two failures at once — the metric is undefined (visual? structural? pixel?), and the target is unreachable for the architecture (a local 14B reconstructing from a text digest plateaus ~30% visual; placeholder-vs-real images alone cap it). The fix: define the measure (visual SSIM with a blank-guard), and set a reachable bar (e.g. structural ≥ 85% + visual ≥ 70% with real images), naming "a vision model" as the lever that would raise the ceiling.
- Game-able metric: a blank page scoring 98% because it matched the site's dark background. A target with no robustness guard rewards the dumbest output.
- Single anecdote as a bar: one passing example ≠ a goal. Require ≥2 evals.
- A vibe with no number: "make it feel premium." Either find a measurable proxy or admit it's a taste call, not a goal.
Companion:
meta/learning-loop-rubric.md(scoring what to promote) andmeta/always-recommend.md(lead with the recommendation — including when proposing a reachable target)./goalpursues a goal;/write-a-goalforges one worth pursuing.
What ships with it
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